import pandas as pd
import seaborn as sns
import plotly.express as px
import matplotlib.pyplot as plt
import plotly.io as pio
pio.renderers.default = "plotly_mimetype+notebook"
For this excercise, we have written the following code to load the stock dataset built into plotly express.
stocks = px.data.stocks()
stocks.head()
| date | GOOG | AAPL | AMZN | FB | NFLX | MSFT | |
|---|---|---|---|---|---|---|---|
| 0 | 2018-01-01 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 |
| 1 | 2018-01-08 | 1.018172 | 1.011943 | 1.061881 | 0.959968 | 1.053526 | 1.015988 |
| 2 | 2018-01-15 | 1.032008 | 1.019771 | 1.053240 | 0.970243 | 1.049860 | 1.020524 |
| 3 | 2018-01-22 | 1.066783 | 0.980057 | 1.140676 | 1.016858 | 1.307681 | 1.066561 |
| 4 | 2018-01-29 | 1.008773 | 0.917143 | 1.163374 | 1.018357 | 1.273537 | 1.040708 |
Select a stock and create a suitable plot for it. Make sure the plot is readable with relevant information, such as date, values.
# YOUR CODE HERE
x = (stocks.date)
y = (stocks.GOOG)
fig, ax = plt.subplots(figsize=(15,10))
ax.plot(x,y)
plt.xticks(range(0,len(stocks.date),14))
# set title
ax.set_title('Google stock')
# horizontal axis
ax.set_xlabel('date')
# vertical axis
ax.set_ylabel('stock value')
plt.show()
You've already plot data from one stock. It is possible to plot multiples of them to support comparison.
To highlight different lines, customise line styles, markers, colors and include a legend to the plot.
# YOUR CODE HERE
x = (stocks.date)
y = (stocks.GOOG)
fig, ax = plt.subplots(figsize=(15,10))
ax.plot(x,stocks.GOOG)
ax.plot(x,stocks.AAPL)
ax.plot(x,stocks.AMZN)
ax.plot(x,stocks.FB)
ax.plot(x,stocks.NFLX)
ax.plot(x,stocks.MSFT)
plt.xticks(range(0,len(stocks.date),14))
# set title
ax.set_title('Google stock')
# horizontal axis
ax.set_xlabel('date')
# vertical axis
ax.set_ylabel('stock value')
plt.show()
First, load the tips dataset
tips = sns.load_dataset('tips')
tips.head()
| total_bill | tip | sex | smoker | day | time | size | |
|---|---|---|---|---|---|---|---|
| 0 | 16.99 | 1.01 | Female | No | Sun | Dinner | 2 |
| 1 | 10.34 | 1.66 | Male | No | Sun | Dinner | 3 |
| 2 | 21.01 | 3.50 | Male | No | Sun | Dinner | 3 |
| 3 | 23.68 | 3.31 | Male | No | Sun | Dinner | 2 |
| 4 | 24.59 | 3.61 | Female | No | Sun | Dinner | 4 |
Let's explore this dataset. Pose a question and create a plot that support drawing answers for your question.
Some possible questions:
Are there differences between male and female when it comes to giving tips?
# YOUR CODE HERE
sns.scatterplot(x='sex', y='tip', data=tips)
plt.show()
Redo the above exercises (challenges 2 & 3) with plotly express. Create diagrams which you can interact with.
Hints:
# YOUR CODE HERE
import plotly.express as px
import plotly.graph_objects as go
df = px.data.stocks()
fig1 = px.line(df, x="date", y="GOOG")
fig1.update_traces(line=dict(color = 'orange'))
fig2 = px.line(df, x="date", y="AAPL")
fig2.update_traces(line=dict(color = 'limegreen'))
fig3 = px.line(df, x="date", y="AMZN")
fig3.update_traces(line=dict(color = 'red'))
fig4 = px.line(df, x="date", y="FB")
fig4.update_traces(line=dict(color = 'purple'))
fig5 = px.line(df, x="date", y="NFLX")
fig5.update_traces(line=dict(color = 'cyan'))
fig6 = px.line(df, x="date", y="MSFT")
fig6.update_traces(line=dict(color = 'blue'))
name_list = ['GOOG','AAPL','AMZN','FB','NFLX','MSFT']
fig7 = go.Figure(data=fig1.data + fig2.data + fig3.data + fig4.data + fig5.data + fig6.data)
fig7.update_traces(mode='markers+lines')
fig7.update_xaxes(title_text="date")
fig7.update_yaxes(title_text="value")
fig7.show()
# YOUR CODE HERE
tips = sns.load_dataset('tips')
fig=px.scatter(tips,x='total_bill',y='tip',color='sex',facet_col="smoker",facet_row="time")
fig.show()
Recreate the barplot below that shows the population of different continents for the year 2007.
Hints:
#load data
df = px.data.gapminder()
df.head()
| country | continent | year | lifeExp | pop | gdpPercap | iso_alpha | iso_num | |
|---|---|---|---|---|---|---|---|---|
| 0 | Afghanistan | Asia | 1952 | 28.801 | 8425333 | 779.445314 | AFG | 4 |
| 1 | Afghanistan | Asia | 1957 | 30.332 | 9240934 | 820.853030 | AFG | 4 |
| 2 | Afghanistan | Asia | 1962 | 31.997 | 10267083 | 853.100710 | AFG | 4 |
| 3 | Afghanistan | Asia | 1967 | 34.020 | 11537966 | 836.197138 | AFG | 4 |
| 4 | Afghanistan | Asia | 1972 | 36.088 | 13079460 | 739.981106 | AFG | 4 |
# YOUR CODE HERE
df_2007 = df.query('year==2007')
df_2007
| country | continent | year | lifeExp | pop | gdpPercap | iso_alpha | iso_num | |
|---|---|---|---|---|---|---|---|---|
| 11 | Afghanistan | Asia | 2007 | 43.828 | 31889923 | 974.580338 | AFG | 4 |
| 23 | Albania | Europe | 2007 | 76.423 | 3600523 | 5937.029526 | ALB | 8 |
| 35 | Algeria | Africa | 2007 | 72.301 | 33333216 | 6223.367465 | DZA | 12 |
| 47 | Angola | Africa | 2007 | 42.731 | 12420476 | 4797.231267 | AGO | 24 |
| 59 | Argentina | Americas | 2007 | 75.320 | 40301927 | 12779.379640 | ARG | 32 |
| ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 1655 | Vietnam | Asia | 2007 | 74.249 | 85262356 | 2441.576404 | VNM | 704 |
| 1667 | West Bank and Gaza | Asia | 2007 | 73.422 | 4018332 | 3025.349798 | PSE | 275 |
| 1679 | Yemen, Rep. | Asia | 2007 | 62.698 | 22211743 | 2280.769906 | YEM | 887 |
| 1691 | Zambia | Africa | 2007 | 42.384 | 11746035 | 1271.211593 | ZMB | 894 |
| 1703 | Zimbabwe | Africa | 2007 | 43.487 | 12311143 | 469.709298 | ZWE | 716 |
142 rows × 8 columns
df = px.data.gapminder()
df_2007 = df.query('year==2007')
df_2007_new = df_2007.groupby('continent').sum()
df_2007_new.sort_values("pop",ascending=False,inplace=True)
fig = px.bar(df_2007_new, x="pop", y=df_2007_new.index, color=df_2007_new.index, orientation='h')
fig.show()